Combination of MRI-based prediction and CRISPR/Cas12a-based detection for IDH genotyping in glioma

Early identification of IDH mutation status is of great significance in clinical therapeutic decision-making in the treatment of glioma. We demonstrate a technological solution to improve the accuracy and reliability of IDH mutation detection by combining MRI-based prediction and a CRISPR-based automatic integrated gene detection system (AIGS). A model was constructed to predict the IDH mutation status using whole slices in MRI scans with a Transformer neural network, and the predictive model achieved accuracies of 0.93, 0.87, and 0.84 using the internal and two external test sets, respectively. Additionally, CRISPR/Cas12a-based AIGS was constructed, and AIGS achieved 100% diagnostic accuracy in terms of IDH detection using both frozen tissue and FFPE samples in one hour. Moreover, the feature attribution of our predictive model was assessed using GradCAM, and the highest correlations with tumor cell percentages in enhancing and IDH-wildtype gliomas were found to have GradCAM importance (0.65 and 0.5, respectively). This MRI-based predictive model could, therefore, guide biopsy for tumor-enriched, which would ensure the veracity and stability of the rapid detection results. The combination of our predictive model and AIGS improved the early determination of IDH mutation status in glioma patients. This combined system of MRI-based prediction and CRISPR/Cas12a-based detection can be used to guide biopsy, resection, and radiation for glioma patients to improve patient outcomes.

(IHC) analysis in four in-house sets: ZNH, NTCGH, LPH, and PLAGH sets.To validate or exclude negative IDH-mutation results obtained through IHC in lower-grade gliomas or patients diagnosed at age 55 or younger, sequencing was performed.According to the guidelines of the European Association for Neuro-Oncology [1], patients with a GBM pathological diagnosis and negative IDH-mutation based on IHC who were over the age of 55 were classified as IDH-wildtype.In the case of the TCGA cohort, histological findings and tissue grading submitted to TCGA underwent confirmation by neuropathology review [2].

MRI Image Processing
T1wC images were resampled to a 1mm isovoxel using the 'interpolation' function from the 'SciPy' python package.The resampled T1w, T2w, and FLAIR images were then registered to the isovoxel T1wC using the 'FLIRT' tool from FSL (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki) with 12 degrees of freedom and normalized mutual information as the cost function.The registered and skull-stripped T1wC, T1w, T2w, and FLAIR images were corrected for bias using the ANTs package [3] with the N4 bias correction method.Subsequently, image signal intensity normalization was performed by subtracting the mean value and dividing by the standard deviation of the signal intensity from the entire brain tissue.Finally, the images were resampled to a size of 256 × 256.
The ground truth whole tumor segmentation was performed by a neuroradiologist (Mingjun Li) with 8 years of experience and confirmed by another neurosurgeon (Yilei Xiao) with 11 years of experience.The segmentation of the ground truth was performed using semi-automatic methods based on distinct tumor signal intensity with the open-source software 3D Slicer (https://www.slicer.org/).

Development and Structure of Prediction Model Vision Transformer
Vision Transformer (ViT) was utilized as the backbone architecture for both pre-training and the classification task.A ViT consists of three main components: the patch embedding layer, position embedding, and Transformer blocks [4].
(i) Patch Embedding: The patch embedding layer needs to transform high-dimensional data into sequences.For a 3D volume X∈R H×W×D×C , it is reshaped into a sequence of flattened 3D patches Xp∈R N×(P^3•C) (C is the input channel, (H, W, D) is the input resolution, (P, P, P) is the patch resolution, and N = HWD/P 3 is the number of patches).A trainable linear projection is then applied to map them to patch embeddings.
(ii) Position Embedding: In order to retain positional information, position embeddings are added to the patch embeddings.The standard ViT utilizes 1D learnable position embeddings.
(iii) Transformer Block: The Transformer block consists of alternating layers of multiheaded self-attention [5] and MLP blocks.

Self-Pre-training with Masked Autoencoders
The encoder, decoder and loss function in Masked Autoencoders (MAE) [6] were illustrated as follows: (i) Encoder in MAE.The input image is first divided into non-overlapping patches.These patches are then randomly grouped into visible patches and masked patches.The encoder in MAE, which is a ViT, only operates on the visible patches.
(ii) Decoder in MAE.The decoder in MAE takes the full set of tokens as input, including patchwise representations from the encoder and learnable mask tokens placed in the positions of masked patches.By adding positional embeddings to all the input tokens, the decoder is able to restore the patches in each specific masked position.To reduce the pre-training budget, the decoder is designed to be more lightweight.
(iii) Loss function in MAE.MAE is trained using a reconstruction loss by predicting the pixel values of the masked patches.
(iv) MAE was pre-trained with a batch size of 32, weight decay of 0.05, and training epoch of 800 using Stochastic Gradient Descent (SGD) for optimization.Intermediate weights were saved for each 100 epochs and tested to determine the one with the best reconstruction performance.

Architecture for classification task
A linear classifier was added after extracting the class token output from the Vision Transformer (ViT).To address the classification task, we utilized T1wC, T1w, T2w, and FLAIR images as inputs to the network.The patch size was set to 16 × 16.Each image type was individually processed through the network, generating corresponding feature maps.Averaging the four feature maps, we obtained a combined map before performing the final classification.To ensure stable training of the classification head, we implemented a layer-wise learning rate decay strategy.During training, we used a batch size of 16 and employed stochastic gradient descent (SGD) for optimization.The maximum number of training epochs was set to 150, and we saved the model with the highest accuracy on the validation set.

Fabrication of CRISPR/Cas12a-based AIGS
A compact detection device, based on CRISPR/Cas12a and designed using SolidWorks and Mentor software, was created.The device measures 160×210×155 mm and incorporates three main functions: temperature control, photoexcitation, and fluorescence signal recording.To achieve these functions, the device contains five modules: (i) A temperature control system.(ii) Optical components, such as a 470-nm LED and 470-and 520-nm band-pass filters.The 470nm LED is used for fluorescence excitation, while the 470-nm band-pass filter filters stray light and the 520-nm band-pass filter reduces background interference.The 470-nm LED, 470-and 520-nm band-pass filters were purchased from CREE, while the photodetector was purchased from OSRAM.The shell of the device was manufactured by Suzhou Daqian Model, and the control motherboard was produced by Jiangsu Jialichuang Electronic Technology.

The computation procedure of GradCAM importance
In the heatmaps generated using GradCAM, the intensity of the color was used to quantify the important region.The scaled intensity was calculated by subtracting the Green value from the Red value.It was normalized to a range of 0-1.In addition, the intensity of some areas not covered by GradCAM was considered −1.The formulas are as follows: Then, the correlation between GradCAM importance and tumor cell percentage was analyzed using Spearman's rank correlation coefficient.

Droplet Digital PCR
In order to compare the molecular characteristics of different samples from patient 06, droplet digital PCR (ddPCR) was conducted using the protocol provided by the manufacturer, Sniper Medical Technology Co., Ltd.A total of 80ng DNA was used as input for the experiment.The ddPCR reactions were carried out in 22 μL volumes, with 500 nM of primers and 250 nM of both a fluorescein (FAM)-labeled R132 wildtype probe and a victoria (VIC)-labeled mutant R132H probe.The reaction mixture was then partitioned into nanoliter-sized droplets using the automated DQ24 Droplet Generator.PCR amplification was performed with the following cycling conditions: an initial stage of enzyme activation at 95⁰C for 15 minutes, followed by 40 cycles of denaturation at 95⁰C for 15 seconds and annealing/extension at 60⁰C for 1 minute.
The reaction was completed with a final stage of enzyme deactivation at 98⁰C for 10 minutes.
To identify droplets positive for FAM and/or VIC signal, the PCR product was analyzed using a droplet reader.Poisson distribution analysis was used to quantify the fractional abundance of the IDH1-R132H mutation in the sample: The primer sequences used for ddPCR are listed below.

Whole Exome Sequencing
To investigate the heterogeneity in IDH-wildtype gliomas, we performed whole exome sequencing (WES) on the tumor samples.The tumor samples were processed at Harbin Xingyun Biological Information Technology Development Co., Ltd., where genomic DNA from the tumor was fragmented and underwent end-repair, a-tailing, adapter ligation, and PCR.
Paired-end (PE) sequencing was then conducted using the DNBSEQ-T7RS instrument, generating 150 bases from each end of the fragments.The resulting PE reads were aligned to the human genome (hg38) using the Burrows-Wheeler Aligner, and the sam files were converted to bam format and sorted by coordinates using Samtools.Duplicate reads were marked using Samblaster, and base quality score recalibration was performed with GAKT.The quality of the alignment was evaluated using Qualimap.To identify somatic variants, including point mutations, small insertions, and deletions, Mutect2 in GATK, CNVkit, and Manta were used.The resulting VCF files were annotated with VEP to extract variant-related information, such as the start and end position of the gene.Furthermore, the purity of each tumor sample was estimated based on the CNV data using the R package ABSOLUTE (v.1.0.6) [7].
To identify candidate somatic mutations, an alignment filter was applied to exclude quality failed reads, unpaired reads, and poorly mapped reads in the tumor.A base quality filter was applied to include bases with a reported Phred quality score >20 for the tumor samples.Somatic mutation candidates were identified based on the following filter conditions: (i) mutations with a low depth (allele depth, <20) were excluded; (ii) mutations with a low allele fraction of alternate alleles in the tumor (allele frequency, <0.1) were excluded; (iii) mutations with supporting reads greater than or equal to 5 were selected; (iv) mutations with a PASS in the filter were selected; (v) mutations with a minor allele frequency (MAF) less than 0.0001 in populations were excluded; (vi) mutations reported as benign or likely benign were excluded; (vii) intronic and silent changes were excluded, while mutations resulting in missense mutations, nonsense mutations, frameshifts, or splice site alterations were retained.The qualified mutations from each sample were used to construct phylogenetic trees.Specifically, sequences encompassing the mutations in a total length of 20 bp were extracted to infer the phylogeny among samples of each patient, using the maximum parsimony algorithm in MEGA [8] IDH (iii) A photodetector and signal processing board for fluorescence collection.(iv) An MCU and various auxiliary components for generating curves based on collected fluorescence.(v) A control board for temperature regulation and LED activation.To enable intelligent operation, an app was developed using Android Studio.The app serves two main functions: (i) Fluorescence signal analysis.This involves recording and displaying the fluorescence intensities for each channel through the serial port.(ii) Timing control.Serial port commands are used to control the timing sequence of each component.The workflow of the app follows this sequence: send a command to the control board > initiate rapid PCR > 30 minutes later, open the valve of the reaction tube and shake it > simultaneously record the fluorescence signals > 30 minutes later, switch off the heat-controlled system and LED light.